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1. A Bayesian Network Approach for Modeling Resident Physician Vigilance

   https://doi.org/10.1177/10711813251369394  

   Prendez, David; Kettel, Veronika; Higgins, Elizabeth; Li, Jiaxin; Kwon, Younghoon; Roach, Victoria; Kim, Ji-Eun (September 2025, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Vigilance, the ability to sustain attention, is critical in healthcare, yet resident physicians face significant sleep deprivation, increasing their risk of vigilance decrement and medical errors. This study aimed to develop a predictive model of vigilance in this population using contextual factors, physiological measures, and eye-tracking data. Fifteen resident physicians participated in psychomotor vigilance tests (PVT) under sleep-deprived and non-sleep-deprived conditions, and completed questionnaires assessing sleep, anxiety, and workload. Bayesian Networks (BN) were employed to model vigilance, featuring layers for contextual factors (sleep, anxiety), performance (PVT reaction time), and observable features (eye movement, physiological responses). The three-layered BN integrating both contextual and multi-sensor (eye-tracking and physiological) data demonstrated the best prediction accuracy, compared to BNs with fewer layers and/or only one sensor type. This demonstrates that combining continuous physiological and eye-tracking data with contextual information enhances the prediction of vigilance decrement in resident physicians. This study contributes to the development of predictive tools for mitigating vigilance decrement and the future design of intervention strategies in demanding clinical settings. 

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2. What Radio Waves Tell Us about Sleep

   He, H; Li, C; Ganglberger, W; Gallagher, K; Hristov, R; Ouroutzoglou, M; Sun, H; Sun, J; Westover, B; Katabi, D (May 2024, arXivorg)

   The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases. 

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3. Real-time synchronization of flight simulation and physiological data using Lab Streaming Layer: A custom X-Plane approach

   Alarcon-Aneiva, Luis J; Fala, N (May 2025, International Symposium on Aviation Psychology)

   Poor synchronization of physiological and flight data hinders human factors research by obscuring cause-effect relationships and requiring laborious manual alignment. We developed a custom X-Plane plugin which uses Lab Streaming Layer (LSL) for real-time synchronization to integrate flight data with simultaneously streamed ECG and eye-tracking signals. Our setup simplifies data collection and ensures precise synchronization without manual intervention, reducing error and preprocessing time. This paper describes the system configuration, plugin, and advantages over conventional methods. Our approach enables accurate correlation between physiological responses and in-flight events, offering deeper insights into pilot performance and workload, resulting in a practical, reproducible method of simplifying multimodal data collection for accessible and efficient research in aviation human factors. 

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4. PhyMask: Robust Sensing of Brain Activity and Physiological Signals During Sleep with an All-textile Eye Mask

   https://doi.org/10.1145/3513023  

   Rostaminia, Soha; Homayounfar, Seyedeh Zohreh; Kiaghadi, Ali; Andrew, Trisha; Ganesan, Deepak (July 2022, ACM Transactions on Computing for Healthcare)

   Clinical-grade wearable sleep monitoring is a challenging problem since it requires concurrently monitoring brain activity, eye movement, muscle activity, cardio-respiratory features, and gross body movements. This requires multiple sensors to be worn at different locations as well as uncomfortable adhesives and discrete electronic components to be placed on the head. As a result, existing wearables either compromise comfort or compromise accuracy in tracking sleep variables. We propose PhyMask, an all-textile sleep monitoring solution that is practical and comfortable for continuous use and that acquires all signals of interest to sleep solely using comfortable textile sensors placed on the head. We show that PhyMask can be used to accurately measure all the signals required for precise sleep stage tracking and to extract advanced sleep markers such as spindles and K-complexes robustly in the real-world setting. We validate PhyMask against polysomnography (PSG) and show that it significantly outperforms two commercially-available sleep tracking wearables—Fitbit and Oura Ring. 

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5. SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks

   https://doi.org/10.1145/3643508  

   Khalid, Maryam; Klerman, Elizabeth B; McHill, Andrew W; Phillips, Andrew_J K; Sano, Akane (March 2024, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Sleep behavior significantly impacts health and acts as an indicator of physical and mental well-being. Monitoring and predicting sleep behavior with ubiquitous sensors may therefore assist in both sleep management and tracking of related health conditions. While sleep behavior depends on, and is reflected in the physiology of a person, it is also impacted by external factors such as digital media usage, social network contagion, and the surrounding weather. In this work, we propose SleepNet, a system that exploits social contagion in sleep behavior through graph networks and integrates it with physiological and phone data extracted from ubiquitous mobile and wearable devices for predicting next-day sleep labels about sleep duration. Our architecture overcomes the limitations of large-scale graphs containing connections irrelevant to sleep behavior by devising an attention mechanism. The extensive experimental evaluation highlights the improvement provided by incorporating social networks in the model. Additionally, we conduct robustness analysis to demonstrate the system's performance in real-life conditions. The outcomes affirm the stability of SleepNet against perturbations in input data. Further analyses emphasize the significance of network topology in prediction performance revealing that users with higher eigenvalue centrality are more vulnerable to data perturbations. 

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